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1UNIT 2 RULES OF UNIVERSAL INSTANTIATION AND GENERALIZATION, EXISTENTIAL INSTANTIATION AND GENERALIZATION, AND RULES OF QUANTIFIER EQUIVALENCE Contents 2.0 Objectives 2.1 Introduction 2.2 Rules of
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How to fill out lifted inference and learning
01
Define the graphical model that represents the problem.
02
Identify the variables and factors involved in the model.
03
Use lifted inference algorithms to efficiently perform inference on the model.
04
Use lifted learning algorithms to learn the parameters of the model from data.
Who needs lifted inference and learning?
01
Researchers and practitioners in the field of machine learning and artificial intelligence.
02
Individuals working on complex probabilistic models that involve a large number of variables and factors.
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What is lifted inference and learning?
Lifted inference and learning is a framework for performing probabilistic reasoning and learning efficiently in relational domains.
Who is required to file lifted inference and learning?
Researchers, data scientists, or anyone working with relational data who wants to perform efficient probabilistic reasoning and learning.
How to fill out lifted inference and learning?
To fill out lifted inference and learning, one needs to model the relational data, define probabilistic queries, and apply efficient inference algorithms.
What is the purpose of lifted inference and learning?
The purpose of lifted inference and learning is to enable efficient probabilistic reasoning and learning in relational domains by exploiting symmetries and shared structures.
What information must be reported on lifted inference and learning?
Information such as relational data models, probabilistic queries, and the results of probabilistic reasoning and learning algorithms must be reported on lifted inference and learning.
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